






'''simple docstring'''







import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase     :   int	      =		logging.getLogger(__name__)




@dataclass
class 		UpperCamelCase__     :




		'''simple docstring'''





		_UpperCamelCase						=       field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
		_UpperCamelCase						=       field(
		    default=a ,metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
		_UpperCamelCase						=       field(
		    default='NER' ,metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}      )
		_UpperCamelCase						=       field(
		    default=a ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
		_UpperCamelCase						=       field(default=a ,metadata={'help': 'Set this flag to use fast tokenization.'}      )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		_UpperCamelCase						=       field(
		    default=a ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,)




@dataclass
class 		UpperCamelCase__     :




		'''simple docstring'''





		_UpperCamelCase						=       field(
		    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}      )
		_UpperCamelCase						=       field(
		    default=a ,metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} ,)
		_UpperCamelCase						=       field(
		    default=128 ,metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    } ,)
		_UpperCamelCase						=       field(
		    default=a ,metadata={'help': 'Overwrite the cached training and evaluation sets'}      )



def     A__      (					):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowerCamelCase__								=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
						if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
						else:
												lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir    )
						    and os.listdir(training_args.output_dir    )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    """ --overwrite_output_dir to overcome."""    )

						lowerCamelCase__								=	import_module("""tasks"""    )
						try:
												lowerCamelCase__								=	getattr(__lowerCAmelCase ,     model_args.task_type    )
												lowerCamelCase__								=	token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
												    F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''    )

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,     datefmt="""%m/%d/%Y %H:%M:%S""" ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,     )
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" ,     training_args.local_rank ,     training_args.device ,     training_args.n_gpu ,     bool(training_args.local_rank != -1    ) ,     training_args.fpaa ,     )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank    ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("""Training/evaluation parameters %s""" ,     __lowerCAmelCase    )

						# Set seed
						set_seed(training_args.seed    )

						# Prepare CONLL-2003 task
						lowerCamelCase__								=	token_classification_task.get_labels(data_args.labels    )
						lowerCamelCase__								=	dict(enumerate(__lowerCAmelCase    )    )
						lowerCamelCase__								=	len(__lowerCAmelCase    )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowerCamelCase__								=	AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,     num_labels=__lowerCAmelCase ,     idalabel=__lowerCAmelCase ,     labelaid={label: i for i, label in enumerate(__lowerCAmelCase    )} ,     cache_dir=model_args.cache_dir ,     )
						lowerCamelCase__								=	AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,     cache_dir=model_args.cache_dir ,     use_fast=model_args.use_fast ,     )
						lowerCamelCase__								=	AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path ,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path    ) ,     config=__lowerCAmelCase ,     cache_dir=model_args.cache_dir ,     )

						# Get datasets
						lowerCamelCase__								=	(
						    TokenClassificationDataset(
						        token_classification_task=__lowerCAmelCase ,     data_dir=data_args.data_dir ,     tokenizer=__lowerCAmelCase ,     labels=__lowerCAmelCase ,     model_type=config.model_type ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     mode=Split.train ,     )
						    if training_args.do_train
						    else None
						)
						lowerCamelCase__								=	(
						    TokenClassificationDataset(
						        token_classification_task=__lowerCAmelCase ,     data_dir=data_args.data_dir ,     tokenizer=__lowerCAmelCase ,     labels=__lowerCAmelCase ,     model_type=config.model_type ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     mode=Split.dev ,     )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(__lowerCAmelCase		:      np.ndarray ,     __lowerCAmelCase		:      np.ndarray    ) -> Tuple[List[int], List[int]]:
												lowerCamelCase__								=	np.argmax(__lowerCAmelCase ,     axis=2    )

												lowerCamelCase__							,      lowerCamelCase__								=	preds.shape

												lowerCamelCase__								=	[[] for _ in range(__lowerCAmelCase    )]
												lowerCamelCase__								=	[[] for _ in range(__lowerCAmelCase    )]

												for i in range(__lowerCAmelCase    ):
																		for j in range(__lowerCAmelCase    ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]    )
																														preds_list[i].append(label_map[preds[i][j]]    )

												return preds_list, out_label_list

						def compute_metrics(__lowerCAmelCase		:      EvalPrediction    ) -> Dict:
												lowerCamelCase__							,      lowerCamelCase__								=	align_predictions(p.predictions ,     p.label_ids    )
												return {
												    "accuracy_score": accuracy_score(__lowerCAmelCase ,     __lowerCAmelCase    ),
												    "precision": precision_score(__lowerCAmelCase ,     __lowerCAmelCase    ),
												    "recall": recall_score(__lowerCAmelCase ,     __lowerCAmelCase    ),
												    "f1": fa_score(__lowerCAmelCase ,     __lowerCAmelCase    ),
												}

						# Data collator
						lowerCamelCase__								=	DataCollatorWithPadding(__lowerCAmelCase ,     pad_to_multiple_of=8    ) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCamelCase__								=	Trainer(
						    model=__lowerCAmelCase ,     args=__lowerCAmelCase ,     train_dataset=__lowerCAmelCase ,     eval_dataset=__lowerCAmelCase ,     compute_metrics=__lowerCAmelCase ,     data_collator=__lowerCAmelCase ,     )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
						lowerCamelCase__								=	{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""    )

												lowerCamelCase__								=	trainer.evaluate()

												lowerCamelCase__								=	os.path.join(training_args.output_dir ,     """eval_results.txt"""    )
												if trainer.is_world_process_zero():
																		with open(__lowerCAmelCase ,     """w"""    ) as writer:
																								logger.info("""***** Eval results *****"""    )
																								for key, value in result.items():
																														logger.info("""  %s = %s""" ,     __lowerCAmelCase ,     __lowerCAmelCase    )
																														writer.write("""%s = %s\n""" % (key, value)    )

																		results.update(__lowerCAmelCase    )

    # Predict
						if training_args.do_predict:
												lowerCamelCase__								=	TokenClassificationDataset(
												    token_classification_task=__lowerCAmelCase ,     data_dir=data_args.data_dir ,     tokenizer=__lowerCAmelCase ,     labels=__lowerCAmelCase ,     model_type=config.model_type ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     mode=Split.test ,     )

												lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	trainer.predict(__lowerCAmelCase    )
												lowerCamelCase__							,      lowerCamelCase__								=	align_predictions(__lowerCAmelCase ,     __lowerCAmelCase    )

												lowerCamelCase__								=	os.path.join(training_args.output_dir ,     """test_results.txt"""    )
												if trainer.is_world_process_zero():
																		with open(__lowerCAmelCase ,     """w"""    ) as writer:
																								for key, value in metrics.items():
																														logger.info("""  %s = %s""" ,     __lowerCAmelCase ,     __lowerCAmelCase    )
																														writer.write("""%s = %s\n""" % (key, value)    )

        # Save predictions
												lowerCamelCase__								=	os.path.join(training_args.output_dir ,     """test_predictions.txt"""    )
												if trainer.is_world_process_zero():
																		with open(__lowerCAmelCase ,     """w"""    ) as writer:
																								with open(os.path.join(data_args.data_dir ,     """test.txt"""    ) ,     """r"""    ) as f:
																														token_classification_task.write_predictions_to_file(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )

						return results
def     A__      (					__lowerCAmelCase		:      Union[str, Any]    ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()



"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





